Nina Kottler
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
- Radiology practices and education
- Radiation Dose and Imaging
- COVID-19 diagnosis using AI
Papers in
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- Artificial Intelligence in Healthcare and Education 9
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- Radiology practices and education 7
- Radiomics and Machine Learning in Medical Imaging 3
- Co-authors
- Bibb Allen (5 shared papers)Elmar Kotter (4 shared papers)Adrian P. Brady (4 shared papers)An Tang (4 shared papers)Christoph Wald (4 shared papers)Lauren Oakden‐Rayner (4 shared papers)Daniel Pinto dos Santos (4 shared papers)John Mongan (4 shared papers)
- Journals
- Journal of the American College of Radiology (3 papers)Insights into Imaging (1 paper)Radiology Artificial Intelligence (1 paper)Academic Radiology (1 paper)American Journal of Roentgenology (1 paper)
- Partner nations
- United StatesCanadaAustralia
In The Last Decade
Nina Kottler
10 papers receiving 205 citations
Peers
Comparison fields: 5 of 43
- Health Informatics 120
- Radiology, Nuclear Medicine and Imaging 102
- Family Practice 4
- Internal Medicine 5
- Critical Care and Intensive Care Medicine 5
Countries citing papers authored by Nina Kottler
This map shows the geographic impact of Nina Kottler's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nina Kottler with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nina Kottler more than expected).
Fields of papers citing papers by Nina Kottler
This network shows the impact of papers produced by Nina Kottler. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nina Kottler. The network helps show where Nina Kottler may publish in the future.
Co-authors
The 25 scholars most cited alongside Nina Kottler, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 77 | |
| 2 | 2024 | 43 | |
| 3 | 2024 | 29 | |
| 4 | 2024 | 25 | |
| 5 | 2024 | 13 | |
| 6 | 2024 | 8 | |
| 7 | 2020 | 8 | |
| 8 | 2020 | 2 | |
| 9 | 2024 | 1 | |
| 10 | 2005 | 1 | |
| 11 | 2025 | 0 | |
| 12 | 2026 | 0 | |
| 13 | 2005 | 0 |
About Nina Kottler
Nina Kottler is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering, Surgery and Neurology, having authored 13 papers that have together received 207 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (9 papers), Radiology practices and education (7 papers), Medical Imaging and Analysis (6 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Appendicitis Diagnosis and Management (1 paper), Diverticular Disease and Complications (1 paper), Advanced X-ray and CT Imaging (1 paper) and Gastrointestinal Tumor Research and Treatment (1 paper). The work is most often cited by research in Health Informatics (120 citations), Radiology, Nuclear Medicine and Imaging (102 citations), Family Practice (4 citations), Internal Medicine (5 citations) and Critical Care and Intensive Care Medicine (5 citations). Nina Kottler has collaborated with scholars based in United States, Canada and Australia. Frequent co-authors include Bibb Allen, Elmar Kotter, Adrian P. Brady, An Tang, Christoph Wald, Lauren Oakden‐Rayner, Daniel Pinto dos Santos, John Mongan, Jaron Chong and Walter F. Wiggins. Their work appears in journals such as Journal of the American College of Radiology, Insights into Imaging, Radiology Artificial Intelligence, Academic Radiology and American Journal of Roentgenology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.